{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LELzIUsVB2NS"
      },
      "source": [
        "![Roboflow Notebooks banner](https://camo.githubusercontent.com/aec53c2b5fb6ed43d202a0ab622b58ba68a89d654fbe3abab0c0cc8bd1ff424e/68747470733a2f2f696b2e696d6167656b69742e696f2f726f626f666c6f772f6e6f7465626f6f6b732f74656d706c6174652f62616e6e657274657374322d322e706e673f696b2d73646b2d76657273696f6e3d6a6176617363726970742d312e342e33267570646174656441743d31363732393332373130313934)\n",
        "\n",
        "# Image Classification with DINOv2\n",
        "\n",
        "DINOv2, released by Meta Research in April 2023, implements a self-supervised method of training computer vision models.\n",
        "\n",
        "DINOv2 was trained using 140 million images without labels. The embeddings generated by DINOv2 can be used for classification, image retrieval, segmentation, and depth estimation. With that said, Meta Research did not release heads for segmentation and depth estimation.\n",
        "\n",
        "In this guide, we are going to build an image classifier using embeddings from DINOv2. To do so, we will:\n",
        "\n",
        "1. Load a folder of images\n",
        "2. Compute embeddings for each image\n",
        "3. Save all the embeddings in a file and vector store\n",
        "4. Train an SVM classifier to classify images\n",
        "\n",
        "We'll be using the [MIT Indoor Scene Recognition dataset](https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/) in this project, but you can use any labelled classification dataset you have.\n",
        "\n",
        "By the end of this notebook, we'll have a classifier trained on our dataset.\n",
        "\n",
        "Without further ado, let's begin!"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "q82ae19kDpbW"
      },
      "source": [
        "## Import Packages\n",
        "\n",
        "First, let's import the packages we will need for this project."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torchvision.transforms as T\n",
        "from PIL import Image\n",
        "import os\n",
        "import cv2\n",
        "import json\n",
        "import glob\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "hj0EP7A3D7Zv"
      },
      "source": [
        "## Load Data\n",
        "\n",
        "In this guide, we're going to work with the [MIT Indoor Scene Recognition dataset](https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition), hosted on Roboflow Universe. To download this dataset, you will need a [free Roboflow account](https://app.roboflow.com).\n",
        "\n",
        "Let's download the dataset and create a dictionary that maps each image in our training dataset to its associated label."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLWPNzNjLc4p",
        "outputId": "195e5c37-8103-48b2-aab9-f0459fbafa1d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install roboflow supervision -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BPT7w3mEKrf",
        "outputId": "d5933c70-e17e-498a-94ef-89b58d8172ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Downloading Dataset Version Zip in MIT-Indoor-Scene-Recognition-5 to folder: 99% [471924736 / 472310488] bytes"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Extracting Dataset Version Zip to MIT-Indoor-Scene-Recognition-5 in folder:: 100%|██████████| 15712/15712 [00:04<00:00, 3502.21it/s]\n"
          ]
        }
      ],
      "source": [
        "import roboflow\n",
        "import supervision as sv\n",
        "\n",
        "roboflow.login()\n",
        "\n",
        "rf = roboflow.Roboflow()\n",
        "\n",
        "project = rf.workspace(\"popular-benchmarks\").project(\"mit-indoor-scene-recognition\")\n",
        "dataset = project.version(5).download(\"folder\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "064ynDDhCU2i"
      },
      "outputs": [],
      "source": [
        "cwd = os.getcwd()\n",
        "\n",
        "ROOT_DIR = os.path.join(cwd, \"MIT-Indoor-Scene-Recognition-5/train\")\n",
        "\n",
        "labels = {}\n",
        "\n",
        "for folder in os.listdir(ROOT_DIR):\n",
        "    for file in os.listdir(os.path.join(ROOT_DIR, folder)):\n",
        "        if file.endswith(\".jpg\"):\n",
        "            full_name = os.path.join(ROOT_DIR, folder, file)\n",
        "            labels[full_name] = folder\n",
        "\n",
        "files = labels.keys()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "z1NvdxMOEUnH"
      },
      "source": [
        "## Load the Model and Compute Embeddings\n",
        "\n",
        "To train our classifier, we need:\n",
        "\n",
        "1. The embeddings associated with each image in our dataset, and;\n",
        "2. The labels associated with each image.\n",
        "\n",
        "To calculate embeddings, we'll use DINOv2. Below, we load the smallest DINOv2 weights and define functions that will load and compute embeddings for every image in a specified list.\n",
        "\n",
        "We store all of our vectors in a dictionary that is saved to disk so we can reference them again if needed. Note that in production environments one may opt for using another data structure such as a vector embedding database (i.e. faiss) for storing embeddings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rF7rqN44CVLV",
        "outputId": "6289270a-b80a-48b1-8989-e7b26e08d020"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/facebookresearch_dinov2_main\n"
          ]
        }
      ],
      "source": [
        "dinov2_vits14 = torch.hub.load(\"facebookresearch/dinov2\", \"dinov2_vits14\")\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "dinov2_vits14.to(device)\n",
        "\n",
        "transform_image = T.Compose([T.ToTensor(), T.Resize(244), T.CenterCrop(224), T.Normalize([0.5], [0.5])])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "T0woC0J7CX-z"
      },
      "outputs": [],
      "source": [
        "def load_image(img: str) -> torch.Tensor:\n",
        "    \"\"\"\n",
        "    Load an image and return a tensor that can be used as an input to DINOv2.\n",
        "    \"\"\"\n",
        "    img = Image.open(img)\n",
        "\n",
        "    transformed_img = transform_image(img)[:3].unsqueeze(0)\n",
        "\n",
        "    return transformed_img\n",
        "\n",
        "def compute_embeddings(files: list) -> dict:\n",
        "    \"\"\"\n",
        "    Create an index that contains all of the images in the specified list of files.\n",
        "    \"\"\"\n",
        "    all_embeddings = {}\n",
        "    \n",
        "    with torch.no_grad():\n",
        "      for i, file in enumerate(tqdm(files)):\n",
        "        embeddings = dinov2_vits14(load_image(file).to(device))\n",
        "\n",
        "        all_embeddings[file] = np.array(embeddings[0].cpu().numpy()).reshape(1, -1).tolist()\n",
        "\n",
        "    with open(\"all_embeddings.json\", \"w\") as f:\n",
        "        f.write(json.dumps(all_embeddings))\n",
        "\n",
        "    return all_embeddings"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "dTqbdE17FIGl"
      },
      "source": [
        "## Compute Embeddings\n",
        "\n",
        "The code below computes the embeddings for all the images in our dataset. This step will take a few minutes for the MIT Indoor Scene Recognition dataset. There are over 10,000 images in the training set that we need to pass through DINOv2."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "657a33c5a717440ebbda246f3d4a12c8",
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            "5eb177c2edf442a1ac41ea08b1585d22",
            "8dfd1e4ffe524b7f9fab579e7778dfd3",
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            "92cb5bcc25d1488d95a2e816d82bf0b0",
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            "75e65f34115e4766bb3e459e240a9b74",
            "adb24cfd48d44513859004803b3df172"
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        },
        "id": "WDMx6fBVCqib",
        "outputId": "9f6f4892-6060-407d-e349-7afb9e7ffefe"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "657a33c5a717440ebbda246f3d4a12c8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/10885 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "embeddings = compute_embeddings(files)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "nK-mPpkuFWJX"
      },
      "source": [
        "## Train a Classification Model\n",
        "\n",
        "The embeddings we have computed can be used as an input in a classification model. For this guide, we will be using SVM, a linear classification model.\n",
        "\n",
        "Below, we make lists of both all of the embeddings we have computed and their associated labels. We then fit our model using those lists."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "tHrpE_-pCu9z",
        "outputId": "bb951a6c-a24f-423e-aa76-0f29c76f8e6f"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10885\n"
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          "data": {
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              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SVC()"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC(gamma='scale')\n",
        "\n",
        "y = [labels[file] for file in files]\n",
        "\n",
        "embedding_list = list(embeddings.values())\n",
        "\n",
        "clf.fit(np.array(embedding_list).reshape(-1, 384), y)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "JOytKjPdFqwD"
      },
      "source": [
        "## Classify an Image\n",
        "\n",
        "We now have a classifier we can use to classify images!\n",
        "\n",
        "Change the `input_file` value below to the path of a file in the `valid` or `test` directories in the image dataset with which we have been working.\n",
        "\n",
        "Then, run the cell to classify the image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "peRLdL1DC5lN",
        "outputId": "134b9e92-a73a-4e55-9e28-32903ecfdb7c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=416x416 at 0x7F3D6F8316F0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Predicted class: elevator\n"
          ]
        }
      ],
      "source": [
        "input_file = \"MIT-Indoor-Scene-Recognition-5/test/elevator/elevator_google_0053_jpg.rf.41487c3b9c1690a5de26ee0218452627.jpg\"\n",
        "\n",
        "new_image = load_image(input_file)\n",
        "\n",
        "%matplotlib inline\n",
        "sv.plot_image(image=cv2.imread(input_file), size=(8, 8))\n",
        "\n",
        "with torch.no_grad():\n",
        "    embedding = dinov2_vits14(new_image.to(device))\n",
        "\n",
        "    prediction = clf.predict(np.array(embedding[0].cpu()).reshape(1, -1))\n",
        "\n",
        "    print()\n",
        "    print(\"Predicted class: \" + prediction[0])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "8JGg1q8eC1L_"
      },
      "source": [
        "## 🏆 Congratulations\n",
        "\n",
        "### Learning Resources\n",
        "\n",
        "Roboflow has produced many resources that you may find interesting as you advance your knowledge of computer vision:\n",
        "\n",
        "- [Roboflow Notebooks](https://github.com/roboflow/notebooks): A repository of over 20 notebooks that walk through how to train custom models with a range of model types, from YOLOv7 to SegFormer.\n",
        "- [Roboflow YouTube](https://www.youtube.com/c/Roboflow): Our library of videos featuring deep dives into the latest in computer vision, detailed tutorials that accompany our notebooks, and more.\n",
        "- [Roboflow Discuss](https://discuss.roboflow.com/): Have a question about how to do something on Roboflow? Ask your question on our discussion forum.\n",
        "- [Roboflow Models](https://roboflow.com): Learn about state-of-the-art models and their performance. Find links and tutorials to guide your learning.\n",
        "\n",
        "### Convert data formats\n",
        "\n",
        "Roboflow provides free utilities to convert data between dozens of popular computer vision formats. Check out [Roboflow Formats](https://roboflow.com/formats) to find tutorials on how to convert data between formats in a few clicks.\n",
        "\n",
        "### Connect computer vision to your project logic\n",
        "\n",
        "[Roboflow Templates](https://roboflow.com/templates) is a public gallery of code snippets that you can use to connect computer vision to your project logic. Code snippets range from sending emails after inference to measuring object distance between detections."
      ]
    }
  ],
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